Work

Enhancing Ammonia Concentration Prediction with a Transfer-Learning-Based Model: Application in a Pig Farm

Year: 2026

Type: article

Abstract: Globally, the swine industry is a major component of agricultural production, and the increasing scale and intensification of pig farming have heightened concerns about NH3 emissions. As farms expand and adopt smart farming technologies, there is a need for reliable prediction of NH3 concentrations without relying solely on costly physical sensors. In this study, we developed an artificial intelligence-based prediction model for NH3 concentration in commercial pig houses and examined the effects of data collection intervals and learning strategies. We compared a standalone model trained only on local data with a transfer learning model that adapts a pre-trained model to a target farm with limited data. Transfer learning consistently outperformed the standalone approach across all data collection intervals (10, 20, 30 and 60 min). The best-performing Random Forest and XGBoost models achieved a coefficient of determination (R2) of 0.969, root mean square error (RMSE) of about 1.0 ppm and mean absolute percentage error (MAPE) below 5%. These results show that transfer learning can provide accurate NH3 predictions in swine housing even with sparse data, supporting more sustainable and data-efficient environmental management.

Source: Animals

DOI: 10.3390/ani16040609

Authors: Sunhyoung Lee, Rack-Woo Kim, Hakjong Shin, Sang-Shin Lee, Won-Gi Choi

Institutions: Kongju National University, Electronics and Telecommunications Research Institute

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $2,896